Overview

Dataset statistics

Number of variables11
Number of observations254974
Missing cells0
Missing cells (%)0.0%
Duplicate rows25742
Duplicate rows (%)10.1%
Total size in memory23.3 MiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

Dataset has 25742 (10.1%) duplicate rowsDuplicates
energy_100g is highly overall correlated with fat_100g and 3 other fieldsHigh correlation
salt_100g is highly overall correlated with sodium_100gHigh correlation
sodium_100g is highly overall correlated with salt_100gHigh correlation
fat_100g is highly overall correlated with energy_100g and 2 other fieldsHigh correlation
saturated_fat_100g is highly overall correlated with energy_100g and 3 other fieldsHigh correlation
nutrition_score_uk_100g is highly overall correlated with energy_100g and 3 other fieldsHigh correlation
nutrition_score_fr_100g is highly overall correlated with energy_100g and 2 other fieldsHigh correlation
cholesterol_100g is highly skewed (γ1 = 293.6387501)Skewed
energy_100g has 6091 (2.4%) zerosZeros
salt_100g has 36339 (14.3%) zerosZeros
sodium_100g has 36343 (14.3%) zerosZeros
fiber_100g has 124820 (49.0%) zerosZeros
additives_n has 82104 (32.2%) zerosZeros
sugars_100g has 50158 (19.7%) zerosZeros
fat_100g has 78545 (30.8%) zerosZeros
saturated_fat_100g has 96736 (37.9%) zerosZeros
nutrition_score_uk_100g has 12521 (4.9%) zerosZeros
nutrition_score_fr_100g has 11704 (4.6%) zerosZeros
cholesterol_100g has 200361 (78.6%) zerosZeros

Reproduction

Analysis started2024-06-07 15:45:05.055387
Analysis finished2024-06-07 15:45:25.825806
Duration20.77 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

energy_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3817
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131.0096
Minimum0
Maximum3700
Zeros6091
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:25.912575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84
Q1393
median1117
Q31674
95-th percentile2389
Maximum3700
Range3700
Interquartile range (IQR)1281

Descriptive statistics

Standard deviation782.9411
Coefficient of variation (CV)0.69224972
Kurtosis-0.50182767
Mean1131.0096
Median Absolute Deviation (MAD)657
Skewness0.40962332
Sum2.8837805 × 108
Variance612996.77
MonotonicityNot monotonic
2024-06-07T17:45:26.054196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6091
 
2.4%
2092 5074
 
2.0%
1674 4010
 
1.6%
1494 3912
 
1.5%
1644 3276
 
1.3%
1393 3219
 
1.3%
1046 2943
 
1.2%
1569 2824
 
1.1%
1795 2347
 
0.9%
1197 2312
 
0.9%
Other values (3807) 218966
85.9%
ValueCountFrequency (%)
0 6091
2.4%
0.02 1
 
< 0.1%
0.42 1
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 7
 
< 0.1%
0.9 4
 
< 0.1%
0.92 4
 
< 0.1%
1 49
 
< 0.1%
1.1 1
 
< 0.1%
ValueCountFrequency (%)
3700 256
0.1%
3699 5
 
< 0.1%
3697 1
 
< 0.1%
3696 3
 
< 0.1%
3693 6
 
< 0.1%
3692 1
 
< 0.1%
3691 1
 
< 0.1%
3690 3
 
< 0.1%
3689 2
 
< 0.1%
3686 1
 
< 0.1%

salt_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5500
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5783321
Minimum0
Maximum100
Zeros36339
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:26.201802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05334
median0.56388
Q31.36144
95-th percentile4
Maximum100
Range100
Interquartile range (IQR)1.3081

Descriptive statistics

Standard deviation6.2312293
Coefficient of variation (CV)3.9479835
Kurtosis141.59793
Mean1.5783321
Median Absolute Deviation (MAD)0.54388
Skewness11.077406
Sum402433.66
Variance38.828219
MonotonicityNot monotonic
2024-06-07T17:45:26.339433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36339
 
14.3%
0.01 3564
 
1.4%
0.1 3305
 
1.3%
1 2153
 
0.8%
0.0254 2091
 
0.8%
1.27 1938
 
0.8%
1.63322 1824
 
0.7%
0.127 1775
 
0.7%
0.03 1558
 
0.6%
0.02032 1537
 
0.6%
Other values (5490) 198890
78.0%
ValueCountFrequency (%)
0 36339
14.3%
5 × 10-81
 
< 0.1%
9.999999 × 10-82
 
< 0.1%
1 × 10-61
 
< 0.1%
5 × 10-61
 
< 0.1%
7.874 × 10-61
 
< 0.1%
1 × 10-55
 
< 0.1%
1.3 × 10-54
 
< 0.1%
2 × 10-51
 
< 0.1%
2.413 × 10-51
 
< 0.1%
ValueCountFrequency (%)
100 21
 
< 0.1%
99.93 1
 
< 0.1%
99.90582 111
< 0.1%
99.9 8
 
< 0.1%
99.822 5
 
< 0.1%
99.8 3
 
< 0.1%
99.78644 10
 
< 0.1%
99.64674 3
 
< 0.1%
99.568 1
 
< 0.1%
99.5 1
 
< 0.1%

sodium_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5251
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64268795
Minimum0
Maximum100
Zeros36343
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:26.491028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.021
median0.222
Q30.536
95-th percentile1.583
Maximum100
Range100
Interquartile range (IQR)0.515

Descriptive statistics

Standard deviation2.6506021
Coefficient of variation (CV)4.1242443
Kurtosis162.08847
Mean0.64268795
Median Absolute Deviation (MAD)0.21412598
Skewness11.525525
Sum163868.72
Variance7.0256914
MonotonicityNot monotonic
2024-06-07T17:45:27.014627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36343
 
14.3%
0.003937007874 3559
 
1.4%
0.03937007874 3290
 
1.3%
0.3937007874 2138
 
0.8%
0.01 2090
 
0.8%
0.5 1936
 
0.8%
0.643 1847
 
0.7%
0.01181102362 1842
 
0.7%
0.05 1775
 
0.7%
0.008 1541
 
0.6%
Other values (5241) 198613
77.9%
ValueCountFrequency (%)
0 36343
14.3%
1.968503937 × 10-81
 
< 0.1%
3.93700748 × 10-82
 
< 0.1%
3.937007874 × 10-71
 
< 0.1%
1.968503937 × 10-61
 
< 0.1%
3.1 × 10-61
 
< 0.1%
3.937007874 × 10-65
 
< 0.1%
5.118110236 × 10-64
 
< 0.1%
7.874015748 × 10-61
 
< 0.1%
9.5 × 10-61
 
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
92.5 1
< 0.1%
83 1
< 0.1%
75 2
< 0.1%
74 1
< 0.1%
71.429 1
< 0.1%
70 1
< 0.1%
62.5 1
< 0.1%
60.3 1
< 0.1%
59 1
< 0.1%

fiber_100g
Real number (ℝ)

ZEROS 

Distinct1010
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2069436
Minimum0
Maximum100
Zeros124820
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:27.196142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.3
Q33
95-th percentile9.6
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.219424
Coefficient of variation (CV)1.9118857
Kurtosis58.132747
Mean2.2069436
Median Absolute Deviation (MAD)0.3
Skewness5.4282559
Sum562713.25
Variance17.803539
MonotonicityNot monotonic
2024-06-07T17:45:27.344745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124820
49.0%
3.6 8511
 
3.3%
3.3 3986
 
1.6%
1.8 3874
 
1.5%
0.8 3799
 
1.5%
7.1 3703
 
1.5%
1.6 3413
 
1.3%
2 3399
 
1.3%
1.2 3259
 
1.3%
2.4 3221
 
1.3%
Other values (1000) 92989
36.5%
ValueCountFrequency (%)
0 124820
49.0%
0.0001 2
 
< 0.1%
0.0002 1
 
< 0.1%
0.001 16
 
< 0.1%
0.002 3
 
< 0.1%
0.004 1
 
< 0.1%
0.00416 1
 
< 0.1%
0.005 2
 
< 0.1%
0.01 72
 
< 0.1%
0.016 1
 
< 0.1%
ValueCountFrequency (%)
100 10
< 0.1%
99 1
 
< 0.1%
94.8 1
 
< 0.1%
92.4 1
 
< 0.1%
90 1
 
< 0.1%
88 2
 
< 0.1%
87.5 1
 
< 0.1%
87 1
 
< 0.1%
86.2 1
 
< 0.1%
85.2 1
 
< 0.1%

additives_n
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7051856
Minimum-1
Maximum31
Zeros82104
Zeros (%)32.2%
Negative25244
Negative (%)9.9%
Memory size3.9 MiB
2024-06-07T17:45:27.476393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median1
Q33
95-th percentile7
Maximum31
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5527784
Coefficient of variation (CV)1.4970677
Kurtosis6.8785431
Mean1.7051856
Median Absolute Deviation (MAD)1
Skewness2.0516666
Sum434778
Variance6.5166775
MonotonicityNot monotonic
2024-06-07T17:45:27.601059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 82104
32.2%
1 44028
17.3%
2 35091
13.8%
-1 25244
 
9.9%
3 22854
 
9.0%
4 14640
 
5.7%
5 10392
 
4.1%
6 6876
 
2.7%
7 4395
 
1.7%
8 3171
 
1.2%
Other values (22) 6179
 
2.4%
ValueCountFrequency (%)
-1 25244
 
9.9%
0 82104
32.2%
1 44028
17.3%
2 35091
13.8%
3 22854
 
9.0%
4 14640
 
5.7%
5 10392
 
4.1%
6 6876
 
2.7%
7 4395
 
1.7%
8 3171
 
1.2%
ValueCountFrequency (%)
31 4
 
< 0.1%
29 2
 
< 0.1%
28 2
 
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 11
< 0.1%
24 10
 
< 0.1%
23 14
< 0.1%
22 26
< 0.1%
21 20
< 0.1%

sugars_100g
Real number (ℝ)

ZEROS 

Distinct4052
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.10314
Minimum0
Maximum100
Zeros50158
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:27.737716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median4.8
Q322.22
95-th percentile60.774
Maximum100
Range100
Interquartile range (IQR)21.42

Descriptive statistics

Standard deviation20.84872
Coefficient of variation (CV)1.3804228
Kurtosis2.4900988
Mean15.10314
Median Absolute Deviation (MAD)4.8
Skewness1.7375937
Sum3850908
Variance434.66911
MonotonicityNot monotonic
2024-06-07T17:45:27.872361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50158
 
19.7%
3.57 7142
 
2.8%
0.5 4359
 
1.7%
3.33 3700
 
1.5%
1 2567
 
1.0%
20 2323
 
0.9%
6.67 2264
 
0.9%
10 2168
 
0.9%
50 2101
 
0.8%
7.14 2024
 
0.8%
Other values (4042) 176168
69.1%
ValueCountFrequency (%)
0 50158
19.7%
0.0001 8
 
< 0.1%
0.0005 1
 
< 0.1%
0.001 24
 
< 0.1%
0.0019 2
 
< 0.1%
0.0048 1
 
< 0.1%
0.007 1
 
< 0.1%
0.01 85
 
< 0.1%
0.0108 1
 
< 0.1%
0.02 27
 
< 0.1%
ValueCountFrequency (%)
100 921
0.4%
99.95 1
 
< 0.1%
99.9 6
 
< 0.1%
99.8 3
 
< 0.1%
99.7 10
 
< 0.1%
99.6 4
 
< 0.1%
99.5 10
 
< 0.1%
99.3 2
 
< 0.1%
99.2 3
 
< 0.1%
99 40
 
< 0.1%

fat_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3370
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.979729
Minimum0
Maximum100
Zeros78545
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:28.006004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.79
Q319
95-th percentile45
Maximum100
Range100
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.247552
Coefficient of variation (CV)1.439728
Kurtosis6.5670472
Mean11.979729
Median Absolute Deviation (MAD)3.79
Skewness2.2702762
Sum3054519.5
Variance297.47804
MonotonicityNot monotonic
2024-06-07T17:45:28.142611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78545
30.8%
25 3375
 
1.3%
32.14 2978
 
1.2%
0.5 2967
 
1.2%
20 2656
 
1.0%
1.79 2526
 
1.0%
28.57 2459
 
1.0%
21.43 2411
 
0.9%
0.1 2367
 
0.9%
10 2238
 
0.9%
Other values (3360) 152452
59.8%
ValueCountFrequency (%)
0 78545
30.8%
0.0001 2
 
< 0.1%
0.000133 1
 
< 0.1%
0.001 1
 
< 0.1%
0.003 1
 
< 0.1%
0.004 2
 
< 0.1%
0.005 3
 
< 0.1%
0.007 1
 
< 0.1%
0.01 42
 
< 0.1%
0.012 2
 
< 0.1%
ValueCountFrequency (%)
100 1280
0.5%
99.9 16
 
< 0.1%
99.85 1
 
< 0.1%
99.82 1
 
< 0.1%
99.8 17
 
< 0.1%
99.7 5
 
< 0.1%
99.4 5
 
< 0.1%
99 5
 
< 0.1%
98.73 1
 
< 0.1%
98.5 1
 
< 0.1%

saturated_fat_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2192
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5437063
Minimum0
Maximum100
Zeros96736
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:28.289218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.58
95-th percentile19
Maximum100
Range100
Interquartile range (IQR)6.58

Descriptive statistics

Standard deviation7.6251009
Coefficient of variation (CV)1.6781677
Kurtosis23.985088
Mean4.5437063
Median Absolute Deviation (MAD)1
Skewness3.6365845
Sum1158527
Variance58.142164
MonotonicityNot monotonic
2024-06-07T17:45:28.441810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96736
37.9%
0.1 5227
 
2.1%
3.57 3480
 
1.4%
0.5 3079
 
1.2%
7.14 2875
 
1.1%
0.2 2558
 
1.0%
1 2335
 
0.9%
0.3 2302
 
0.9%
3.33 2211
 
0.9%
1.79 2189
 
0.9%
Other values (2182) 131982
51.8%
ValueCountFrequency (%)
0 96736
37.9%
0.0001 11
 
< 0.1%
0.001 30
 
< 0.1%
0.002 10
 
< 0.1%
0.003 4
 
< 0.1%
0.0032 1
 
< 0.1%
0.004 3
 
< 0.1%
0.005 11
 
< 0.1%
0.006 2
 
< 0.1%
0.00667 1
 
< 0.1%
ValueCountFrequency (%)
100 12
< 0.1%
99.9 1
 
< 0.1%
99 2
 
< 0.1%
98 1
 
< 0.1%
96 2
 
< 0.1%
95.5 1
 
< 0.1%
95 5
< 0.1%
94 2
 
< 0.1%
93.8 1
 
< 0.1%
93.33 3
 
< 0.1%

nutrition_score_uk_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22209
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0157221
Minimum-15
Maximum40
Zeros12521
Zeros (%)4.9%
Negative37141
Negative (%)14.6%
Memory size3.9 MiB
2024-06-07T17:45:28.574455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-4
Q12
median9
Q316
95-th percentile24
Maximum40
Range55
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.7620999
Coefficient of variation (CV)0.97186889
Kurtosis-0.86589005
Mean9.0157221
Median Absolute Deviation (MAD)7
Skewness0.19504308
Sum2298774.7
Variance76.774394
MonotonicityNot monotonic
2024-06-07T17:45:28.710092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12521
 
4.9%
1 11784
 
4.6%
2 10932
 
4.3%
14 10565
 
4.1%
-1 8730
 
3.4%
13 8309
 
3.3%
12 8140
 
3.2%
11 7980
 
3.1%
3 7489
 
2.9%
20 7301
 
2.9%
Other values (22199) 161223
63.2%
ValueCountFrequency (%)
-15 12
 
< 0.1%
-14 5
 
< 0.1%
-13 23
 
< 0.1%
-12.98946843 1
 
< 0.1%
-12 46
< 0.1%
-11.89017976 1
 
< 0.1%
-11.71202015 1
 
< 0.1%
-11.11047737 1
 
< 0.1%
-11.02351469 1
 
< 0.1%
-11 90
< 0.1%
ValueCountFrequency (%)
40 33
< 0.1%
39.62928274 27
< 0.1%
39.57922926 14
< 0.1%
38.90178138 7
 
< 0.1%
38.74275539 1
 
< 0.1%
38 1
 
< 0.1%
37.86717433 3
 
< 0.1%
37.22136799 1
 
< 0.1%
37.13967297 9
 
< 0.1%
37 2
 
< 0.1%

nutrition_score_fr_100g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22224
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1377468
Minimum-15
Maximum40
Zeros11704
Zeros (%)4.6%
Negative35481
Negative (%)13.9%
Memory size3.9 MiB
2024-06-07T17:45:28.846726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-4
Q12
median9
Q315.613139
95-th percentile24
Maximum40
Range55
Interquartile range (IQR)13.613139

Descriptive statistics

Standard deviation8.6223733
Coefficient of variation (CV)0.9435995
Kurtosis-0.81166515
Mean9.1377468
Median Absolute Deviation (MAD)7
Skewness0.16934547
Sum2329887.9
Variance74.345322
MonotonicityNot monotonic
2024-06-07T17:45:28.989346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11704
 
4.6%
1 11130
 
4.4%
14 11124
 
4.4%
2 10457
 
4.1%
13 8725
 
3.4%
-1 8707
 
3.4%
12 8558
 
3.4%
11 8537
 
3.3%
3 7725
 
3.0%
15 7457
 
2.9%
Other values (22214) 160850
63.1%
ValueCountFrequency (%)
-15 1
 
< 0.1%
-14.56626981 1
 
< 0.1%
-14.53003013 1
 
< 0.1%
-14.43958461 1
 
< 0.1%
-14.42682726 1
 
< 0.1%
-14.42473158 2
 
< 0.1%
-14.38149172 2
 
< 0.1%
-14.34924791 3
 
< 0.1%
-14 5
 
< 0.1%
-13 23
< 0.1%
ValueCountFrequency (%)
40 4
 
< 0.1%
38.5578851 1
 
< 0.1%
38.51842973 1
 
< 0.1%
38.49152462 3
 
< 0.1%
38.4492283 1
 
< 0.1%
38.44860451 1
 
< 0.1%
38.44609177 20
< 0.1%
38.43481639 2
 
< 0.1%
38.41384923 1
 
< 0.1%
38.09412942 27
< 0.1%

cholesterol_100g
Real number (ℝ)

SKEWED  ZEROS 

Distinct535
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011335686
Minimum0
Maximum95.238
Zeros200361
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:29.128972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.071
Maximum95.238
Range95.238
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26935215
Coefficient of variation (CV)23.761433
Kurtosis91156.825
Mean0.011335686
Median Absolute Deviation (MAD)0
Skewness293.63875
Sum2890.3053
Variance0.07255058
MonotonicityNot monotonic
2024-06-07T17:45:29.265607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 200361
78.6%
0.071 2461
 
1.0%
0.107 2235
 
0.9%
0.012 1908
 
0.7%
0.089 1662
 
0.7%
0.054 1651
 
0.6%
0.018 1587
 
0.6%
0.004 1503
 
0.6%
0.036 1385
 
0.5%
0.008 1209
 
0.5%
Other values (525) 39012
 
15.3%
ValueCountFrequency (%)
0 200361
78.6%
4.5 × 10-51
 
< 0.1%
7.1 × 10-51
 
< 0.1%
0.0001 5
 
< 0.1%
0.0002 5
 
< 0.1%
0.0004 1
 
< 0.1%
0.000416 1
 
< 0.1%
0.00046 1
 
< 0.1%
0.0005 2
 
< 0.1%
0.0008 1
 
< 0.1%
ValueCountFrequency (%)
95.238 1
< 0.1%
70.588 1
< 0.1%
62.5 1
< 0.1%
13.846 1
< 0.1%
10.9 1
< 0.1%
1.58 1
< 0.1%
1.291 1
< 0.1%
1.25 1
< 0.1%
1.081 1
< 0.1%
0.996 1
< 0.1%

Interactions

2024-06-07T17:45:23.614721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:08.291706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:09.850537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:11.727517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:12.985160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:14.277696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:16.013058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:17.418298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:18.990091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:20.521994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:22.158616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:23.747368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:08.504138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:10.550665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:11.847198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:13.105829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:14.416325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:16.159661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:17.757388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:19.146672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:20.661622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:22.330157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:23.880011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:08.629803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:10.664361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:11.954910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-06-07T17:45:12.172328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-06-07T17:45:14.806283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-06-07T17:45:09.573281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:11.489154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:12.756764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:14.011409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:15.686927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:17.181928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:18.744749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:20.260693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:21.848445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:23.368382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:24.933194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:09.701935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:11.616814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:12.874449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:14.136075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:15.848494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:17.306594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:18.865425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:20.395333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:22.020986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:23.495041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-06-07T17:45:29.355367image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
energy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gcholesterol_100g
energy_100g1.000-0.088-0.0910.247-0.0070.3050.7160.5470.6430.6200.009
salt_100g-0.0881.0000.926-0.035-0.002-0.096-0.051-0.0430.1570.153-0.000
sodium_100g-0.0910.9261.000-0.036-0.006-0.093-0.052-0.0440.1460.143-0.000
fiber_100g0.247-0.035-0.0361.000-0.114-0.0220.0960.003-0.151-0.158-0.015
additives_n-0.007-0.002-0.006-0.1141.0000.144-0.063-0.0670.1300.1320.001
sugars_100g0.305-0.096-0.093-0.0220.1441.000-0.0690.0610.4290.443-0.013
fat_100g0.716-0.051-0.0520.096-0.063-0.0691.0000.6330.5320.5050.023
saturated_fat_100g0.547-0.043-0.0440.003-0.0670.0610.6331.0000.6310.6130.034
nutrition_score_uk_100g0.6430.1570.146-0.1510.1300.4290.5320.6311.0000.9870.027
nutrition_score_fr_100g0.6200.1530.143-0.1580.1320.4430.5050.6130.9871.0000.027
cholesterol_100g0.009-0.000-0.000-0.0150.001-0.0130.0230.0340.0270.0271.000
2024-06-07T17:45:29.503970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
additives_ncholesterol_100genergy_100gfat_100gfiber_100gnutrition_score_fr_100gnutrition_score_uk_100gsalt_100gsaturated_fat_100gsodium_100gsugars_100g
additives_n1.0000.1590.0130.086-0.1000.1640.1630.142-0.0380.1420.182
cholesterol_100g0.1591.0000.0630.310-0.1570.1980.2030.2260.3490.226-0.086
energy_100g0.0130.0631.0000.6470.2790.6270.6460.0970.5980.0960.263
fat_100g0.0860.3100.6471.0000.1860.4830.5070.2500.7590.250-0.067
fiber_100g-0.100-0.1570.2790.1861.000-0.215-0.2090.0070.0700.0070.106
nutrition_score_fr_100g0.1640.1980.6270.483-0.2151.0000.9850.3020.6090.3020.345
nutrition_score_uk_100g0.1630.2030.6460.507-0.2090.9851.0000.3260.6300.3260.321
salt_100g0.1420.2260.0970.2500.0070.3020.3261.0000.2301.000-0.245
saturated_fat_100g-0.0380.3490.5980.7590.0700.6090.6300.2301.0000.2290.045
sodium_100g0.1420.2260.0960.2500.0070.3020.3261.0000.2291.000-0.245
sugars_100g0.182-0.0860.263-0.0670.1060.3450.321-0.2450.045-0.2451.000

Missing values

2024-06-07T17:45:25.063847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-07T17:45:25.341104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

energy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gcholesterol_100g
02243.00.000000.0003.60.014.2928.5728.5714.00000014.0000000.018
11941.00.635000.2507.10.017.8617.860.000.0000000.0000000.000
22540.01.224280.4827.10.03.5757.145.3612.00000012.0000000.000
31552.00.000000.0005.70.00.001.430.005.3447335.3237290.000
41933.00.000000.0007.70.011.5418.271.928.0122077.8985410.000
51490.00.000000.0000.00.00.000.000.006.8273556.8093920.000
61833.00.139700.0559.42.015.6218.754.697.0000007.0000000.000
72406.00.000000.0007.50.042.5037.5022.5018.27775418.0600350.000
83586.00.000000.0000.00.00.00100.007.1418.82227917.7653010.000
91393.00.000000.00012.50.00.001.040.002.8747132.9295740.000
energy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gcholesterol_100g
2583131393.00.952500.3750008.31.030.569.722.7811.00000011.0000000.0
2583141084.00.290000.1141730.0-1.010.500.0012.0016.00000016.0000000.0
2583154.010.000003.93700810.0-1.01.000.001.000.0000000.0000000.0
2583161477.00.030480.0120004.70.02.350.000.00-1.000000-1.0000000.0
258317368.00.045720.0180000.08.019.300.000.007.1390067.5680280.0
2583181643.00.680000.2677175.9-1.02.602.800.60-4.000000-4.0000000.0
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Duplicate rows

Most frequently occurring

energy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gcholesterol_100g# duplicates
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